AI-First
Innovation
Evogene is an AI-first company transforming drug discovery through its proprietary ChemPass AI platform, encompassing virtual screening and de novo multi-property molecule design capabilities.
We collaborate with strategic partners, combining capabilities and data to direct and accelerate the discovery of groundbreaking therapies.
To Discover Breakthrough Therapies
Evogene is an AI-first company transforming drug discovery through its proprietary ChemPass AI platform, encompassing virtual screening and de novo multi-property molecule design capabilities.
By integrating cutting-edge AI, computational chemistry, and life sciences expertise, we accelerate the creation of novel small molecules.
With a proven track record of successful collaborations, we partner with drug developers to drive access to breakthrough therapies.
Pioneer groundbreaking novel therapies based on small molecules to improve global health
We merge life-science expertise with big data and cutting edge AI to efficiently discover and optimise breakthrough therapies
Evogene’s Drug Discovery Unit is at the forefront of innovation in small-molecule drug development, leveraging the company’s accumulated computational biology and chemistry expertise, built over 20 years.
Evogene has demonstrated the application of advanced artificial intelligence capabilities to accelerate the discovery and development of novel products across various life sciences sectors. These range from agriculture to the food industry and now extend into the pharmaceutical sector. These achievements are validated through successful collaborations between Evogene and its subsidiaries with and major industry players.
The primary mission of Evogene’s Drug Discovery Unit is to discover and optimize small molecules as leading candidates for drug development using Evogene’s proprietary ChemPass-AI platform, which represents cutting-edge technologies in computational chemistry.
Head of Product: Ph.D. in Computational Chemistry from Bar-Ilan University. Over a decade of experience, including leading the Drug Design Unit at Tel Aviv University’s Blavatnik Center and managing projects at the Bar-Ilan Institute for Nanotechnology
Product Manager: Computational chemist with a Ph.D. in Structural Biology from Tel Aviv University and an M.Sc. in Molecular Biology from Technion. Over 25 years of experience, including senior roles as CTO at AptuCure Bio Ltd and VP of R&D at Cannasoul Analytics. Skilled in structure-function relationships and molecular modeling.
Computational chemist with a Ph.D. in Chemistry from the Weizmann Institute and a B.Sc. in Chemical Engineering. 20+ years of experience in computational drug discovery and bioinformatics. Co-founder of HQL Pharmaceuticals and Associate Director at EPIX, applying advanced data science to solve complex challenges.
Experienced biotech leader with an M.Sc. and Ph.D. in Biology from the Weizmann Institute Gabi's research focused on the modular regulation of signal transduction networks. Previously, he served as CTO at Fore Biotherapeutics and held senior R&D roles at NovellusDx.
One of the challenges leading to this in-efficiency is the fact that less than 0.1% of chemical space has been explored, leaving vast potential drug candidates undiscovered. Unlocking the secrets of the unexplored 99.9% of chemical space is key to overcoming current limitations in drug discovery specifically for multi-property lead optimization.
At Evogene, we break these barriers with AI-powered de-novo molecular design, unlocking new chemical possibilities, increasing the probability of success, and reducing costly failures.
A deep learning platform leveraging a curated database of 40 billion compounds to identify the most promising candidates
Generative-AI for novel
small molecules (NCEs)
Increase probability of success by
de-risking later phases of drug development (efficacy, ADME, safety)
State-of-the-art AI model,
developed in collaboration with Google Cloud, enhanced de novo design
Integrating chemistry, biology, and AI for optimized drug discovery
Internal capabilities to develop propriety machine learning and deep learning algorithms
Proven ability to work with partners, to translate computational predictions into real-world drug candidates
Utilizing Deep learning to enhance rapid screening in large databases
DeepDock for improved hit rate
DeepDock enables ultra-fast computational screening of a 40-billion compound curated database using our proprietary in-house developed deep learning algorithms. This allows us to:
Upgraded analogue search with propriety algorithem
Active Search for Novel Chemistry
ActiveSearch employs advanced analogue search algorithms that leverage experimentally validated molecules to systematically expand discovery within relevant chemical space:
De-novo design with in-house foundation model
ChemPass-GPT: AI-Powered Molecular Design (De-Novo Drug Creation with AI Guidance)
ChemPass-GPT applies generative AI principles to design completely new molecules that meet complex drug discovery requirements:
Drag to explore
The chemical space of potential drug-like molecules is incredibly vast, estimated to be between 10^20 and 10^60. To put that into perspective, it’s believed that we’ve explored less than 0.1% of this vast chemical universe.
This staggering number highlights how much of this space remains uncharted and underscores the urgent need for more effective discovery methods.
Current AI approaches in drug discovery often face limitations due to:
Small and Imbalanced Datasets: Leading to biased models and limited predictive power.
Redundant and Invalid Molecules: Generating molecules that are not novel or lack drug-like properties.
Lack of Uniqueness: Producing molecules that are too similar to existing compounds, hindering true innovation.
Multi-Parameter Design Capabilities: Allows optimization for multiple drug-relevant parameters (e.g., potency, ADMET) from the start.
Trained on Diverse Datasets: Overcomes small and imbalanced dataset limitations by incorporating broad, chemically diverse data curated in-house.
Novelty-Driven Generation: Explicitly optimized to generate unique, drug-like molecules—avoiding redundancy and invalid candidates.
Full In-House Control: Enables rapid iteration, customization, and integration across pipelines, without dependency on third-party models.
Built for Scalability: Architecture supports ongoing expansion with new data, targets, and properties, ensuring continuous performance improvement.
*Disclaimer: Just how big is the chemical universe really? While the vastness of chemical space is often conveyed through figures like 10^60 molecules and the notion that we’ve explored less than 0.1%, it’s important to acknowledge that these are broad estimations, not definitive figures. They serve to illustrate the immense, largely untapped potential for novel molecular discovery. Our commitment to innovation is driven by this vast landscape of unexplored possibilities, regardless of the precise numbers.
* Alternatively, experimental validation can be conducted in-house by Evogene.
* Alternatively, experimental validation can be conducted in-house by Evogene.
Through years of experience, we’ve refined our approach to seamlessly integrate with partners, ensuring a smooth and efficient workflow from day one.
By working closely with our partners, we continuously generate deep insights that accelerate decision-making and enhance the discovery process.
Our AI-powered platform, combined with your deep scientific expertise, transforms early-stage discoveries into actionable results, driving projects forward with efficiency.
Whether enhancing existing discovery efforts or driving new projects from scratch, we tailor our approach to each partner’s needs.